1. Technical Field
The present disclosure relates to a subject estimation system, a subject estimation method, and a non-transitory recording medium having a computer program stored thereon, the system, method, and program estimating a subject of a dialog.
2. Description of the Related Art
There are systems that perform pattern recognition by utilizing a convolutional neural network (for example, U.S. Patent Application Publication No. 2003/0174881 (hereinafter referred to as “Patent Document 1”)). Patent Document 1 disclose a typical method for pattern recognition using a convolutional neural network.
Methods in which a convolutional neural network is applied to the field of natural-language processing have also been known (e.g., Yoon Kim, “Convolutional Neural Networks for Sentence Classification”, searched on the Internet, URL:http://arxiv.org/abs/1408.5882, on Mar. 29, 2016 (this document is hereinafter referred to as “Non-Patent Document 1”)). Non-Patent Document 1 discloses a method for classifying a sentence by using a convolutional neural network made to perform learning using a known data set.
However, the sentence classification methods using the above-described related art are based on the premise that the convolutional neural network is learned using a sufficient amount of learning data, and much consideration has not been given to cases in which the amount of learning data is not sufficient.
Thus, even when the convolutional neural networks disclosed in the related art are used, there is a drawback in that a task for estimating a subject of a dialog cannot be accurately performed when the amount of learning data is not sufficient.